Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-385
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dc.contributor.authorHärtner, Franziska-
dc.contributor.authorAndrade, Miguel-
dc.contributor.authorAlanis-Lobato, Gregorio-
dc.date.accessioned2018-07-23T10:16:24Z-
dc.date.available2018-07-23T12:16:24Z-
dc.date.issued2018-
dc.identifier.urihttps://openscience.ub.uni-mainz.de/handle/20.500.12030/387-
dc.description.abstractThere is an increasing accumulation of evidence supporting the existence of a hyperbolic geometry underlying the network representation of complex systems. In particular, it has been shown that the latent geometry of the human protein network (hPIN) captures biologically relevant information, leading to a meaningful visual representation of protein-protein interactions and translating challenging systems biology problems into measuring distances between proteins. Moreover, proteins can efficiently communicate with each other, without global knowledge of the hPIN structure, via a greedy routing (GR) process in which hyperbolic distances guide biological signals from source to target proteins. It is thanks to this effective information routing throughout the hPIN that the cell operates, communicates with other cells and reacts to environmental changes. As a result, the malfunction of one or a few members of this intricate system can disturb its dynamics and derive in disease phenotypes. In fact, it is known that the proteins associated with a single disease agglomerate non-randomly in the same region of the hPIN, forming one or several connected components known as the disease module (DM). Here, we present a geometric characterisation of DMs. First, we found that DM positions on the two-dimensional hyperbolic plane reflect their fragmentation and functional heterogeneity, rendering an informative picture of the cellular processes that the disease is affecting. Second, we used a distance-based dissimilarity measure to cluster DMs with shared clinical features. Finally, we took advantage of the GR strategy to study how defective proteins affect the transduction of signals throughout the hPIN.en_GB
dc.description.sponsorshipDFG, Open Access-Publizieren Universität Mainz / Universitätsmedizin-
dc.language.isoeng-
dc.rightsCC BYde_DE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subject.ddc570 Biowissenschaftende_DE
dc.subject.ddc570 Life sciencesen_GB
dc.titleGeometric characterisation of disease modulesen_GB
dc.typeZeitschriftenaufsatzde_DE
dc.identifier.doihttp://doi.org/10.25358/openscience-385-
jgu.type.dinitypearticle-
jgu.type.versionPublished versionen_GB
jgu.type.resourceText-
jgu.organisation.departmentFB 10 Biologie-
jgu.organisation.number7970-
jgu.organisation.nameJohannes Gutenberg-Universität Mainz-
jgu.rights.accessrightsopenAccess-
jgu.journal.titleApplied network science-
jgu.journal.volume3-
jgu.journal.issue1-
jgu.pages.alternativeArt. 10-
jgu.publisher.year2018-
jgu.publisher.nameSpringer International Publishing-
jgu.publisher.placeCham-
jgu.publisher.urihttp://dx.doi.org/10.1007/s41109-018-0066-3-
jgu.publisher.issn2364-8228-
jgu.notes.publicAndrade, Miguel veröffentlicht unter: Andrade-Navarro, Miguel A.de_DE
jgu.organisation.placeMainz-
jgu.subject.ddccode570-
opus.date.accessioned2018-07-23T10:16:24Z-
opus.date.modified2018-07-23T10:30:57Z-
opus.date.available2018-07-23T12:16:24-
opus.subject.dfgcode00-000-
opus.organisation.stringFB 10: Biologie: Institut für Organismische und Molekulare Evolutionsbiologiede_DE
opus.identifier.opusid58368-
opus.institute.number1011-
opus.metadataonlyfalse-
opus.type.contenttypeKeinede_DE
opus.type.contenttypeNoneen_GB
opus.affiliatedAndrade, Miguel-
opus.affiliatedAlanis-Lobato, Gregorio-
jgu.publisher.doi10.1007/s41109-018-0066-3
jgu.organisation.rorhttps://ror.org/023b0x485
Appears in collections:JGU-Publikationen

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